Advancing Modified Barium Swallow Pre-sorting With Deep Learning: A New Paradigm for the First Step Analysis in X-ray Swallowing Study

Authors

Shitong Mao
Mohamed A Naser
Sheila Buoy
Kristy K Brock
Katherine A Hutcheson

Language

English

Publication Date

10-4-2025

Journal

International Journal of Computer Assisted Radiology and Surgery

DOI

10.1007/s11548-025-03505-y

PMID

41046271

PMCID

PMC12551561

PubMedCentral® Posted Date

10-25-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Purpose: Modified barium swallow (MBS) exams are pivotal for assessing swallowing function and include diagnostic video segments imaged in various planes, such as anteroposterior (AP or coronal plane) and lateral (or mid-sagittal plane), alongside non-diagnostic 'scout' image segments used for anatomic reference and image set-up that do not include bolus swallows. These variations in imaging files necessitate manual sorting and labeling, complicating the pre-analysis workflow.

Methods: Our study introduces a deep learning approach to automate the categorization of swallow videos in MBS exams, distinguishing between the different types of diagnostic videos and identifying non-diagnostic scout videos to streamline the MBS review workflow. Our algorithms were developed on a dataset that included 3,740 video segments with a total of 986,808 frames from 285 MBS exams in 216 patients (average age 60 ± 9).

Results: Our model achieved an accuracy of 99.68% at the frame level and 100% at the video level in differentiating AP from lateral planes. For distinguishing scout from bolus swallowing videos, the model reached an accuracy of 90.26% at the frame level and 93.86% at the video level. Incorporating a multi-task learning approach notably enhanced the video-level accuracy to 96.35% for scout/bolus video differentiation.

Conclusion: Our analysis highlighted the importance of leveraging inter-frame connectivity for improving the model performance. These findings significantly boost MBS exam processing efficiency, minimizing manual sorting efforts and allowing raters to allocate greater focus to clinical interpretation and patient care.

Keywords

Modified Barium Swallow, Deep Learning, Convolutional Neural Networks, Swallowing Disorders, Dysphagia, Videofluoroscopy

Published Open-Access

yes

Recommended Citation

Mao, Shitong; Naser, Mohamed A; Buoy, Sheila; et al., "Advancing Modified Barium Swallow Pre-sorting With Deep Learning: A New Paradigm for the First Step Analysis in X-ray Swallowing Study" (2025). Faculty, Staff and Student Publications. 5386.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5386